diff --git a/libs/partners/google-vertexai/.gitignore b/libs/partners/google-vertexai/.gitignore
new file mode 100644
index 00000000000..bee8a64b79a
--- /dev/null
+++ b/libs/partners/google-vertexai/.gitignore
@@ -0,0 +1 @@
+__pycache__
diff --git a/libs/partners/google-vertexai/LICENSE b/libs/partners/google-vertexai/LICENSE
new file mode 100644
index 00000000000..426b6509034
--- /dev/null
+++ b/libs/partners/google-vertexai/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/libs/partners/google-vertexai/Makefile b/libs/partners/google-vertexai/Makefile
new file mode 100644
index 00000000000..ceb9823f9d1
--- /dev/null
+++ b/libs/partners/google-vertexai/Makefile
@@ -0,0 +1,59 @@
+.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
+
+# Default target executed when no arguments are given to make.
+all: help
+
+# Define a variable for the test file path.
+TEST_FILE ?= tests/unit_tests/
+
+test:
+	poetry run pytest $(TEST_FILE)
+
+tests:
+	poetry run pytest $(TEST_FILE)
+
+
+######################
+# LINTING AND FORMATTING
+######################
+
+# Define a variable for Python and notebook files.
+PYTHON_FILES=.
+MYPY_CACHE=.mypy_cache
+lint format: PYTHON_FILES=.
+lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/google-vertexai --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
+lint_package: PYTHON_FILES=langchain_google_vertexai
+lint_tests: PYTHON_FILES=tests
+lint_tests: MYPY_CACHE=.mypy_cache_test
+
+lint lint_diff lint_package lint_tests:
+	poetry run ruff .
+	poetry run ruff format $(PYTHON_FILES) --diff
+	poetry run ruff --select I $(PYTHON_FILES)
+	mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
+
+format format_diff:
+	poetry run ruff format $(PYTHON_FILES)
+	poetry run ruff --select I --fix $(PYTHON_FILES)
+
+spell_check:
+	poetry run codespell --toml pyproject.toml
+
+spell_fix:
+	poetry run codespell --toml pyproject.toml -w
+
+check_imports: $(shell find langchain_google_vertexai -name '*.py')
+	poetry run python ./scripts/check_imports.py $^
+
+######################
+# HELP
+######################
+
+help:
+	@echo '----'
+	@echo 'check_imports				- check imports'
+	@echo 'format                       - run code formatters'
+	@echo 'lint                         - run linters'
+	@echo 'test                         - run unit tests'
+	@echo 'tests                        - run unit tests'
+	@echo 'test TEST_FILE=<test_file>   - run all tests in file'
diff --git a/libs/partners/google-vertexai/README.md b/libs/partners/google-vertexai/README.md
new file mode 100644
index 00000000000..6a4839254fc
--- /dev/null
+++ b/libs/partners/google-vertexai/README.md
@@ -0,0 +1,100 @@
+# langchain-google-vertexai
+
+This package contains the LangChain integrations for Google Cloud generative models.
+
+## Installation
+
+```bash
+pip install -U langchain-google-vertexai
+```
+
+## Chat Models
+
+`ChatVertexAI` class exposes models .
+
+To use, you should have Google Cloud project with APIs enabled, and configured credentials. Initialize the model as:
+
+```python
+from langchain_google_vertexai import ChatVertexAI
+
+llm = ChatVertexAI(model_name="gemini-pro")
+llm.invoke("Sing a ballad of LangChain.")
+```
+
+You can use other models, e.g. `chat-bison`:
+```python
+from langchain_google_vertexai import ChatVertexAI
+
+llm = ChatVertexAI(model_name="chat-bison", temperature=0.3)
+llm.invoke("Sing a ballad of LangChain.")
+```
+
+#### Multimodal inputs
+
+Gemini vision model supports image inputs when providing a single chat message. Example:
+
+```python
+from langchain_core.messages import HumanMessage
+from langchain_google_vertexai import ChatVertexAI
+
+llm = ChatVertexAI(model_name="gemini-pro-vision")
+# example
+message = HumanMessage(
+    content=[
+        {
+            "type": "text",
+            "text": "What's in this image?",
+        },  # You can optionally provide text parts
+        {"type": "image_url", "image_url": {"url": "https://picsum.photos/seed/picsum/200/300"}},
+    ]
+)
+llm.invoke([message])
+```
+
+The value of `image_url` can be any of the following:
+
+- A public image URL
+- An accessible gcs file (e.g., "gcs://path/to/file.png")
+- A local file path
+- A base64 encoded image (e.g., `data:image/png;base64,abcd124`)
+
+
+## Embeddings
+
+You can use Google Cloud's embeddings models as:
+
+```
+from langchain_google_vertexai import VertexAIEmbeddings
+
+embeddings = VertexAIEmbeddings()
+embeddings.embed_query("hello, world!")
+```
+
+## LLMs
+You can use Google Cloud's generative AI models as Langchain LLMs:
+
+```python
+from langchain.prompts import PromptTemplate
+from langchain_google_vertexai import VertexAI
+
+template = """Question: {question}
+
+Answer: Let's think step by step."""
+prompt = PromptTemplate.from_template(template)
+
+chain = prompt | llm
+
+question = "Who was the president in the year Justin Beiber was born?"
+print(chain.invoke({"question": question}))
+```
+
+You can use Gemini and Palm models, including code-generations ones:
+```python
+from langchain_google_vertexai import VertexAI
+
+llm = VertexAI(model_name="code-bison", max_output_tokens=1000, temperature=0.3)
+
+question = "Write a python function that checks if a string is a valid email address"
+
+output = llm(question)
+```
diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/__init__.py b/libs/partners/google-vertexai/langchain_google_vertexai/__init__.py
new file mode 100644
index 00000000000..391a7c7b1d1
--- /dev/null
+++ b/libs/partners/google-vertexai/langchain_google_vertexai/__init__.py
@@ -0,0 +1,5 @@
+from langchain_google_vertexai.chat_models import ChatVertexAI
+from langchain_google_vertexai.embeddings import VertexAIEmbeddings
+from langchain_google_vertexai.llms import VertexAI, VertexAIModelGarden
+
+__all__ = ["ChatVertexAI", "VertexAIEmbeddings", "VertexAI", "VertexAIModelGarden"]
diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py b/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py
new file mode 100644
index 00000000000..6dcc7a2d73c
--- /dev/null
+++ b/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py
@@ -0,0 +1,88 @@
+"""Utilities to init Vertex AI."""
+from importlib import metadata
+from typing import Any, Callable, Optional, Union
+
+import google.api_core
+from google.api_core.gapic_v1.client_info import ClientInfo
+from google.cloud import storage  # type: ignore
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.llms import create_base_retry_decorator
+from vertexai.preview.generative_models import Image  # type: ignore
+
+
+def create_retry_decorator(
+    *,
+    max_retries: int = 1,
+    run_manager: Optional[
+        Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
+    ] = None,
+) -> Callable[[Any], Any]:
+    """Creates a retry decorator for Vertex / Palm LLMs."""
+
+    errors = [
+        google.api_core.exceptions.ResourceExhausted,
+        google.api_core.exceptions.ServiceUnavailable,
+        google.api_core.exceptions.Aborted,
+        google.api_core.exceptions.DeadlineExceeded,
+        google.api_core.exceptions.GoogleAPIError,
+    ]
+    decorator = create_base_retry_decorator(
+        error_types=errors, max_retries=max_retries, run_manager=run_manager
+    )
+    return decorator
+
+
+def raise_vertex_import_error(minimum_expected_version: str = "1.38.0") -> None:
+    """Raise ImportError related to Vertex SDK being not available.
+
+    Args:
+        minimum_expected_version: The lowest expected version of the SDK.
+    Raises:
+        ImportError: an ImportError that mentions a required version of the SDK.
+    """
+    raise ImportError(
+        "Please, install or upgrade the google-cloud-aiplatform library: "
+        f"pip install google-cloud-aiplatform>={minimum_expected_version}"
+    )
+
+
+def get_client_info(module: Optional[str] = None) -> "ClientInfo":
+    r"""Returns a custom user agent header.
+
+    Args:
+        module (Optional[str]):
+            Optional. The module for a custom user agent header.
+    Returns:
+        google.api_core.gapic_v1.client_info.ClientInfo
+    """
+    langchain_version = metadata.version("langchain")
+    client_library_version = (
+        f"{langchain_version}-{module}" if module else langchain_version
+    )
+    return ClientInfo(
+        client_library_version=client_library_version,
+        user_agent=f"langchain/{client_library_version}",
+    )
+
+
+def load_image_from_gcs(path: str, project: Optional[str] = None) -> Image:
+    """Loads im Image from GCS."""
+    gcs_client = storage.Client(project=project)
+    pieces = path.split("/")
+    blobs = list(gcs_client.list_blobs(pieces[2], prefix="/".join(pieces[3:])))
+    if len(blobs) > 1:
+        raise ValueError(f"Found more than one candidate for {path}!")
+    return Image.from_bytes(blobs[0].download_as_bytes())
+
+
+def is_codey_model(model_name: str) -> bool:
+    """Returns True if the model name is a Codey model."""
+    return "code" in model_name
+
+
+def is_gemini_model(model_name: str) -> bool:
+    """Returns True if the model name is a Gemini model."""
+    return model_name is not None and "gemini" in model_name
diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py b/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py
new file mode 100644
index 00000000000..49f28d0bf8c
--- /dev/null
+++ b/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py
@@ -0,0 +1,366 @@
+"""Wrapper around Google VertexAI chat-based models."""
+from __future__ import annotations
+
+import base64
+import logging
+import re
+from dataclasses import dataclass, field
+from typing import Any, Dict, Iterator, List, Optional, Union, cast
+from urllib.parse import urlparse
+
+import requests
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.chat_models import (
+    BaseChatModel,
+    generate_from_stream,
+)
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+    HumanMessage,
+    SystemMessage,
+)
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from langchain_core.pydantic_v1 import root_validator
+from vertexai.language_models import (  # type: ignore
+    ChatMessage,
+    ChatModel,
+    ChatSession,
+    CodeChatModel,
+    CodeChatSession,
+    InputOutputTextPair,
+)
+from vertexai.preview.generative_models import (  # type: ignore
+    Content,
+    GenerativeModel,
+    Image,
+    Part,
+)
+
+from langchain_google_vertexai._utils import (
+    is_codey_model,
+    is_gemini_model,
+    load_image_from_gcs,
+)
+from langchain_google_vertexai.llms import (
+    _VertexAICommon,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class _ChatHistory:
+    """Represents a context and a history of messages."""
+
+    history: List[ChatMessage] = field(default_factory=list)
+    context: Optional[str] = None
+
+
+def _parse_chat_history(history: List[BaseMessage]) -> _ChatHistory:
+    """Parse a sequence of messages into history.
+
+    Args:
+        history: The list of messages to re-create the history of the chat.
+    Returns:
+        A parsed chat history.
+    Raises:
+        ValueError: If a sequence of message has a SystemMessage not at the
+        first place.
+    """
+
+    vertex_messages, context = [], None
+    for i, message in enumerate(history):
+        content = cast(str, message.content)
+        if i == 0 and isinstance(message, SystemMessage):
+            context = content
+        elif isinstance(message, AIMessage):
+            vertex_message = ChatMessage(content=message.content, author="bot")
+            vertex_messages.append(vertex_message)
+        elif isinstance(message, HumanMessage):
+            vertex_message = ChatMessage(content=message.content, author="user")
+            vertex_messages.append(vertex_message)
+        else:
+            raise ValueError(
+                f"Unexpected message with type {type(message)} at the position {i}."
+            )
+    chat_history = _ChatHistory(context=context, history=vertex_messages)
+    return chat_history
+
+
+def _is_url(s: str) -> bool:
+    try:
+        result = urlparse(s)
+        return all([result.scheme, result.netloc])
+    except Exception as e:
+        logger.debug(f"Unable to parse URL: {e}")
+        return False
+
+
+def _parse_chat_history_gemini(
+    history: List[BaseMessage], project: Optional[str]
+) -> List[Content]:
+    def _convert_to_prompt(part: Union[str, Dict]) -> Part:
+        if isinstance(part, str):
+            return Part.from_text(part)
+
+        if not isinstance(part, Dict):
+            raise ValueError(
+                f"Message's content is expected to be a dict, got {type(part)}!"
+            )
+        if part["type"] == "text":
+            return Part.from_text(part["text"])
+        elif part["type"] == "image_url":
+            path = part["image_url"]["url"]
+            if path.startswith("gs://"):
+                image = load_image_from_gcs(path=path, project=project)
+            elif path.startswith("data:image/"):
+                # extract base64 component from image uri
+                try:
+                    regexp = r"data:image/\w{2,4};base64,(.*)"
+                    encoded = re.search(regexp, path).group(1)  # type: ignore
+                except AttributeError:
+                    raise ValueError(
+                        "Invalid image uri. It should be in the format "
+                        "data:image/<image_type>;base64,<base64_encoded_image>."
+                    )
+                image = Image.from_bytes(base64.b64decode(encoded))
+            elif _is_url(path):
+                response = requests.get(path)
+                response.raise_for_status()
+                image = Image.from_bytes(response.content)
+            else:
+                image = Image.load_from_file(path)
+        else:
+            raise ValueError("Only text and image_url types are supported!")
+        return Part.from_image(image)
+
+    vertex_messages = []
+    for i, message in enumerate(history):
+        if i == 0 and isinstance(message, SystemMessage):
+            raise ValueError("SystemMessages are not yet supported!")
+        elif isinstance(message, AIMessage):
+            role = "model"
+        elif isinstance(message, HumanMessage):
+            role = "user"
+        else:
+            raise ValueError(
+                f"Unexpected message with type {type(message)} at the position {i}."
+            )
+
+        raw_content = message.content
+        if isinstance(raw_content, str):
+            raw_content = [raw_content]
+        parts = [_convert_to_prompt(part) for part in raw_content]
+        vertex_message = Content(role=role, parts=parts)
+        vertex_messages.append(vertex_message)
+    return vertex_messages
+
+
+def _parse_examples(examples: List[BaseMessage]) -> List[InputOutputTextPair]:
+    if len(examples) % 2 != 0:
+        raise ValueError(
+            f"Expect examples to have an even amount of messages, got {len(examples)}."
+        )
+    example_pairs = []
+    input_text = None
+    for i, example in enumerate(examples):
+        if i % 2 == 0:
+            if not isinstance(example, HumanMessage):
+                raise ValueError(
+                    f"Expected the first message in a part to be from human, got "
+                    f"{type(example)} for the {i}th message."
+                )
+            input_text = example.content
+        if i % 2 == 1:
+            if not isinstance(example, AIMessage):
+                raise ValueError(
+                    f"Expected the second message in a part to be from AI, got "
+                    f"{type(example)} for the {i}th message."
+                )
+            pair = InputOutputTextPair(
+                input_text=input_text, output_text=example.content
+            )
+            example_pairs.append(pair)
+    return example_pairs
+
+
+def _get_question(messages: List[BaseMessage]) -> HumanMessage:
+    """Get the human message at the end of a list of input messages to a chat model."""
+    if not messages:
+        raise ValueError("You should provide at least one message to start the chat!")
+    question = messages[-1]
+    if not isinstance(question, HumanMessage):
+        raise ValueError(
+            f"Last message in the list should be from human, got {question.type}."
+        )
+    return question
+
+
+class ChatVertexAI(_VertexAICommon, BaseChatModel):
+    """`Vertex AI` Chat large language models API."""
+
+    model_name: str = "chat-bison"
+    "Underlying model name."
+    examples: Optional[List[BaseMessage]] = None
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that the python package exists in environment."""
+        is_gemini = is_gemini_model(values["model_name"])
+        cls._init_vertexai(values)
+        if is_gemini:
+            values["client"] = GenerativeModel(model_name=values["model_name"])
+        else:
+            if is_codey_model(values["model_name"]):
+                model_cls = CodeChatModel
+            else:
+                model_cls = ChatModel
+            values["client"] = model_cls.from_pretrained(values["model_name"])
+        return values
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        stream: Optional[bool] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """Generate next turn in the conversation.
+
+        Args:
+            messages: The history of the conversation as a list of messages. Code chat
+                does not support context.
+            stop: The list of stop words (optional).
+            run_manager: The CallbackManager for LLM run, it's not used at the moment.
+            stream: Whether to use the streaming endpoint.
+
+        Returns:
+            The ChatResult that contains outputs generated by the model.
+
+        Raises:
+            ValueError: if the last message in the list is not from human.
+        """
+        should_stream = stream if stream is not None else self.streaming
+        if should_stream:
+            stream_iter = self._stream(
+                messages, stop=stop, run_manager=run_manager, **kwargs
+            )
+            return generate_from_stream(stream_iter)
+
+        question = _get_question(messages)
+        params = self._prepare_params(stop=stop, stream=False, **kwargs)
+        msg_params = {}
+        if "candidate_count" in params:
+            msg_params["candidate_count"] = params.pop("candidate_count")
+
+        if self._is_gemini_model:
+            history_gemini = _parse_chat_history_gemini(messages, project=self.project)
+            message = history_gemini.pop()
+            chat = self.client.start_chat(history=history_gemini)
+            response = chat.send_message(message, generation_config=params)
+        else:
+            history = _parse_chat_history(messages[:-1])
+            examples = kwargs.get("examples") or self.examples
+            if examples:
+                params["examples"] = _parse_examples(examples)
+            chat = self._start_chat(history, **params)
+            response = chat.send_message(question.content, **msg_params)
+        generations = [
+            ChatGeneration(message=AIMessage(content=r.text))
+            for r in response.candidates
+        ]
+        return ChatResult(generations=generations)
+
+    async def _agenerate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """Asynchronously generate next turn in the conversation.
+
+        Args:
+            messages: The history of the conversation as a list of messages. Code chat
+                does not support context.
+            stop: The list of stop words (optional).
+            run_manager: The CallbackManager for LLM run, it's not used at the moment.
+
+        Returns:
+            The ChatResult that contains outputs generated by the model.
+
+        Raises:
+            ValueError: if the last message in the list is not from human.
+        """
+        if "stream" in kwargs:
+            kwargs.pop("stream")
+            logger.warning("ChatVertexAI does not currently support async streaming.")
+
+        params = self._prepare_params(stop=stop, **kwargs)
+        msg_params = {}
+        if "candidate_count" in params:
+            msg_params["candidate_count"] = params.pop("candidate_count")
+
+        if self._is_gemini_model:
+            history_gemini = _parse_chat_history_gemini(messages, project=self.project)
+            message = history_gemini.pop()
+            chat = self.client.start_chat(history=history_gemini)
+            response = await chat.send_message_async(message, generation_config=params)
+        else:
+            question = _get_question(messages)
+            history = _parse_chat_history(messages[:-1])
+            examples = kwargs.get("examples", None)
+            if examples:
+                params["examples"] = _parse_examples(examples)
+            chat = self._start_chat(history, **params)
+            response = await chat.send_message_async(question.content, **msg_params)
+
+        generations = [
+            ChatGeneration(message=AIMessage(content=r.text))
+            for r in response.candidates
+        ]
+        return ChatResult(generations=generations)
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        params = self._prepare_params(stop=stop, stream=True, **kwargs)
+        if self._is_gemini_model:
+            history_gemini = _parse_chat_history_gemini(messages, project=self.project)
+            message = history_gemini.pop()
+            chat = self.client.start_chat(history=history_gemini)
+            responses = chat.send_message(
+                message, stream=True, generation_config=params
+            )
+        else:
+            question = _get_question(messages)
+            history = _parse_chat_history(messages[:-1])
+            examples = kwargs.get("examples", None)
+            if examples:
+                params["examples"] = _parse_examples(examples)
+            chat = self._start_chat(history, **params)
+            responses = chat.send_message_streaming(question.content, **params)
+        for response in responses:
+            if run_manager:
+                run_manager.on_llm_new_token(response.text)
+            yield ChatGenerationChunk(message=AIMessageChunk(content=response.text))
+
+    def _start_chat(
+        self, history: _ChatHistory, **kwargs: Any
+    ) -> Union[ChatSession, CodeChatSession]:
+        if not self.is_codey_model:
+            return self.client.start_chat(
+                context=history.context, message_history=history.history, **kwargs
+            )
+        else:
+            return self.client.start_chat(message_history=history.history, **kwargs)
diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/embeddings.py b/libs/partners/google-vertexai/langchain_google_vertexai/embeddings.py
new file mode 100644
index 00000000000..041c21fb92b
--- /dev/null
+++ b/libs/partners/google-vertexai/langchain_google_vertexai/embeddings.py
@@ -0,0 +1,336 @@
+import logging
+import re
+import string
+import threading
+from concurrent.futures import ThreadPoolExecutor, wait
+from typing import Any, Dict, List, Literal, Optional, Tuple, Type
+
+from google.api_core.exceptions import (
+    Aborted,
+    DeadlineExceeded,
+    InvalidArgument,
+    ResourceExhausted,
+    ServiceUnavailable,
+)
+from langchain_core.embeddings import Embeddings
+from langchain_core.language_models.llms import create_base_retry_decorator
+from langchain_core.pydantic_v1 import root_validator
+from vertexai.language_models import (  # type: ignore
+    TextEmbeddingInput,
+    TextEmbeddingModel,
+)
+
+from langchain_google_vertexai.llms import _VertexAICommon
+
+logger = logging.getLogger(__name__)
+
+_MAX_TOKENS_PER_BATCH = 20000
+_MAX_BATCH_SIZE = 250
+_MIN_BATCH_SIZE = 5
+
+
+class VertexAIEmbeddings(_VertexAICommon, Embeddings):
+    """Google Cloud VertexAI embedding models."""
+
+    # Instance context
+    instance: Dict[str, Any] = {}  #: :meta private:
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validates that the python package exists in environment."""
+        cls._init_vertexai(values)
+        if values["model_name"] == "textembedding-gecko-default":
+            logger.warning(
+                "Model_name will become a required arg for VertexAIEmbeddings "
+                "starting from Feb-01-2024. Currently the default is set to "
+                "textembedding-gecko@001"
+            )
+            values["model_name"] = "textembedding-gecko@001"
+        values["client"] = TextEmbeddingModel.from_pretrained(values["model_name"])
+        return values
+
+    def __init__(
+        self,
+        # the default value would be removed after Feb-01-2024
+        model_name: str = "textembedding-gecko-default",
+        project: Optional[str] = None,
+        location: str = "us-central1",
+        request_parallelism: int = 5,
+        max_retries: int = 6,
+        credentials: Optional[Any] = None,
+        **kwargs: Any,
+    ):
+        """Initialize the sentence_transformer."""
+        super().__init__(
+            project=project,
+            location=location,
+            credentials=credentials,
+            request_parallelism=request_parallelism,
+            max_retries=max_retries,
+            model_name=model_name,
+            **kwargs,
+        )
+        self.instance["max_batch_size"] = kwargs.get("max_batch_size", _MAX_BATCH_SIZE)
+        self.instance["batch_size"] = self.instance["max_batch_size"]
+        self.instance["min_batch_size"] = kwargs.get("min_batch_size", _MIN_BATCH_SIZE)
+        self.instance["min_good_batch_size"] = self.instance["min_batch_size"]
+        self.instance["lock"] = threading.Lock()
+        self.instance["batch_size_validated"] = False
+        self.instance["task_executor"] = ThreadPoolExecutor(
+            max_workers=request_parallelism
+        )
+        self.instance[
+            "embeddings_task_type_supported"
+        ] = not self.client._endpoint_name.endswith("/textembedding-gecko@001")
+
+    @staticmethod
+    def _split_by_punctuation(text: str) -> List[str]:
+        """Splits a string by punctuation and whitespace characters."""
+        split_by = string.punctuation + "\t\n "
+        pattern = f"([{split_by}])"
+        # Using re.split to split the text based on the pattern
+        return [segment for segment in re.split(pattern, text) if segment]
+
+    @staticmethod
+    def _prepare_batches(texts: List[str], batch_size: int) -> List[List[str]]:
+        """Splits texts in batches based on current maximum batch size
+        and maximum tokens per request.
+        """
+        text_index = 0
+        texts_len = len(texts)
+        batch_token_len = 0
+        batches: List[List[str]] = []
+        current_batch: List[str] = []
+        if texts_len == 0:
+            return []
+        while text_index < texts_len:
+            current_text = texts[text_index]
+            # Number of tokens per a text is conservatively estimated
+            # as 2 times number of words, punctuation and whitespace characters.
+            # Using `count_tokens` API will make batching too expensive.
+            # Utilizing a tokenizer, would add a dependency that would not
+            # necessarily be reused by the application using this class.
+            current_text_token_cnt = (
+                len(VertexAIEmbeddings._split_by_punctuation(current_text)) * 2
+            )
+            end_of_batch = False
+            if current_text_token_cnt > _MAX_TOKENS_PER_BATCH:
+                # Current text is too big even for a single batch.
+                # Such request will fail, but we still make a batch
+                # so that the app can get the error from the API.
+                if len(current_batch) > 0:
+                    # Adding current batch if not empty.
+                    batches.append(current_batch)
+                current_batch = [current_text]
+                text_index += 1
+                end_of_batch = True
+            elif (
+                batch_token_len + current_text_token_cnt > _MAX_TOKENS_PER_BATCH
+                or len(current_batch) == batch_size
+            ):
+                end_of_batch = True
+            else:
+                if text_index == texts_len - 1:
+                    # Last element - even though the batch may be not big,
+                    # we still need to make it.
+                    end_of_batch = True
+                batch_token_len += current_text_token_cnt
+                current_batch.append(current_text)
+                text_index += 1
+            if end_of_batch:
+                batches.append(current_batch)
+                current_batch = []
+                batch_token_len = 0
+        return batches
+
+    def _get_embeddings_with_retry(
+        self, texts: List[str], embeddings_type: Optional[str] = None
+    ) -> List[List[float]]:
+        """Makes a Vertex AI model request with retry logic."""
+
+        errors: List[Type[BaseException]] = [
+            ResourceExhausted,
+            ServiceUnavailable,
+            Aborted,
+            DeadlineExceeded,
+        ]
+        retry_decorator = create_base_retry_decorator(
+            error_types=errors, max_retries=self.max_retries
+        )
+
+        @retry_decorator
+        def _completion_with_retry(texts_to_process: List[str]) -> Any:
+            if embeddings_type and self.instance["embeddings_task_type_supported"]:
+                requests = [
+                    TextEmbeddingInput(text=t, task_type=embeddings_type)
+                    for t in texts_to_process
+                ]
+            else:
+                requests = texts_to_process
+            embeddings = self.client.get_embeddings(requests)
+            return [embs.values for embs in embeddings]
+
+        return _completion_with_retry(texts)
+
+    def _prepare_and_validate_batches(
+        self, texts: List[str], embeddings_type: Optional[str] = None
+    ) -> Tuple[List[List[float]], List[List[str]]]:
+        """Prepares text batches with one-time validation of batch size.
+        Batch size varies between GCP regions and individual project quotas.
+        # Returns embeddings of the first text batch that went through,
+        # and text batches for the rest of the texts.
+        """
+
+        batches = VertexAIEmbeddings._prepare_batches(
+            texts, self.instance["batch_size"]
+        )
+        # If batch size if less or equal to one that went through before,
+        # then keep batches as they are.
+        if len(batches[0]) <= self.instance["min_good_batch_size"]:
+            return [], batches
+        with self.instance["lock"]:
+            # If largest possible batch size was validated
+            # while waiting for the lock, then check for rebuilding
+            # our batches, and return.
+            if self.instance["batch_size_validated"]:
+                if len(batches[0]) <= self.instance["batch_size"]:
+                    return [], batches
+                else:
+                    return [], VertexAIEmbeddings._prepare_batches(
+                        texts, self.instance["batch_size"]
+                    )
+            # Figure out largest possible batch size by trying to push
+            # batches and lowering their size in half after every failure.
+            first_batch = batches[0]
+            first_result = []
+            had_failure = False
+            while True:
+                try:
+                    first_result = self._get_embeddings_with_retry(
+                        first_batch, embeddings_type
+                    )
+                    break
+                except InvalidArgument:
+                    had_failure = True
+                    first_batch_len = len(first_batch)
+                    if first_batch_len == self.instance["min_batch_size"]:
+                        raise
+                    first_batch_len = max(
+                        self.instance["min_batch_size"], int(first_batch_len / 2)
+                    )
+                    first_batch = first_batch[:first_batch_len]
+            first_batch_len = len(first_batch)
+            self.instance["min_good_batch_size"] = max(
+                self.instance["min_good_batch_size"], first_batch_len
+            )
+            # If had a failure and recovered
+            # or went through with the max size, then it's a legit batch size.
+            if had_failure or first_batch_len == self.instance["max_batch_size"]:
+                self.instance["batch_size"] = first_batch_len
+                self.instance["batch_size_validated"] = True
+                # If batch size was updated,
+                # rebuild batches with the new batch size
+                # (texts that went through are excluded here).
+                if first_batch_len != self.instance["max_batch_size"]:
+                    batches = VertexAIEmbeddings._prepare_batches(
+                        texts[first_batch_len:], self.instance["batch_size"]
+                    )
+            else:
+                # Still figuring out max batch size.
+                batches = batches[1:]
+        # Returning embeddings of the first text batch that went through,
+        # and text batches for the rest of texts.
+        return first_result, batches
+
+    def embed(
+        self,
+        texts: List[str],
+        batch_size: int = 0,
+        embeddings_task_type: Optional[
+            Literal[
+                "RETRIEVAL_QUERY",
+                "RETRIEVAL_DOCUMENT",
+                "SEMANTIC_SIMILARITY",
+                "CLASSIFICATION",
+                "CLUSTERING",
+            ]
+        ] = None,
+    ) -> List[List[float]]:
+        """Embed a list of strings.
+
+        Args:
+            texts: List[str] The list of strings to embed.
+            batch_size: [int] The batch size of embeddings to send to the model.
+                If zero, then the largest batch size will be detected dynamically
+                at the first request, starting from 250, down to 5.
+            embeddings_task_type: [str] optional embeddings task type,
+                one of the following
+                    RETRIEVAL_QUERY	- Text is a query
+                                      in a search/retrieval setting.
+                    RETRIEVAL_DOCUMENT - Text is a document
+                                         in a search/retrieval setting.
+                    SEMANTIC_SIMILARITY - Embeddings will be used
+                                          for Semantic Textual Similarity (STS).
+                    CLASSIFICATION - Embeddings will be used for classification.
+                    CLUSTERING - Embeddings will be used for clustering.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        if len(texts) == 0:
+            return []
+        embeddings: List[List[float]] = []
+        first_batch_result: List[List[float]] = []
+        if batch_size > 0:
+            # Fixed batch size.
+            batches = VertexAIEmbeddings._prepare_batches(texts, batch_size)
+        else:
+            # Dynamic batch size, starting from 250 at the first call.
+            first_batch_result, batches = self._prepare_and_validate_batches(
+                texts, embeddings_task_type
+            )
+        # First batch result may have some embeddings already.
+        # In such case, batches have texts that were not processed yet.
+        embeddings.extend(first_batch_result)
+        tasks = []
+        for batch in batches:
+            tasks.append(
+                self.instance["task_executor"].submit(
+                    self._get_embeddings_with_retry,
+                    texts=batch,
+                    embeddings_type=embeddings_task_type,
+                )
+            )
+        if len(tasks) > 0:
+            wait(tasks)
+        for t in tasks:
+            embeddings.extend(t.result())
+        return embeddings
+
+    def embed_documents(
+        self, texts: List[str], batch_size: int = 0
+    ) -> List[List[float]]:
+        """Embed a list of documents.
+
+        Args:
+            texts: List[str] The list of texts to embed.
+            batch_size: [int] The batch size of embeddings to send to the model.
+                If zero, then the largest batch size will be detected dynamically
+                at the first request, starting from 250, down to 5.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        return self.embed(texts, batch_size, "RETRIEVAL_DOCUMENT")
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a text.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embedding for the text.
+        """
+        embeddings = self.embed([text], 1, "RETRIEVAL_QUERY")
+        return embeddings[0]
diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/llms.py b/libs/partners/google-vertexai/langchain_google_vertexai/llms.py
new file mode 100644
index 00000000000..c8905c99a6a
--- /dev/null
+++ b/libs/partners/google-vertexai/langchain_google_vertexai/llms.py
@@ -0,0 +1,469 @@
+from __future__ import annotations
+
+from concurrent.futures import Executor
+from typing import Any, ClassVar, Dict, Iterator, List, Optional, Union
+
+import vertexai  # type: ignore
+from google.api_core.client_options import ClientOptions
+from google.cloud.aiplatform.gapic import (
+    PredictionServiceAsyncClient,
+    PredictionServiceClient,
+)
+from google.cloud.aiplatform.models import Prediction
+from google.protobuf import json_format
+from google.protobuf.struct_pb2 import Value
+from langchain_core.callbacks.manager import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.llms import BaseLLM
+from langchain_core.outputs import Generation, GenerationChunk, LLMResult
+from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
+from vertexai.language_models import (  # type: ignore
+    CodeGenerationModel,
+    TextGenerationModel,
+)
+from vertexai.language_models._language_models import (  # type: ignore
+    TextGenerationResponse,
+)
+from vertexai.preview.generative_models import GenerativeModel, Image  # type: ignore
+from vertexai.preview.language_models import (  # type: ignore
+    CodeGenerationModel as PreviewCodeGenerationModel,
+)
+from vertexai.preview.language_models import (
+    TextGenerationModel as PreviewTextGenerationModel,
+)
+
+from langchain_google_vertexai._utils import (
+    create_retry_decorator,
+    get_client_info,
+    is_codey_model,
+    is_gemini_model,
+)
+
+
+def _completion_with_retry(
+    llm: VertexAI,
+    prompt: List[Union[str, Image]],
+    stream: bool = False,
+    is_gemini: bool = False,
+    run_manager: Optional[CallbackManagerForLLMRun] = None,
+    **kwargs: Any,
+) -> Any:
+    """Use tenacity to retry the completion call."""
+    retry_decorator = create_retry_decorator(
+        max_retries=llm.max_retries, run_manager=run_manager
+    )
+
+    @retry_decorator
+    def _completion_with_retry_inner(
+        prompt: List[Union[str, Image]], is_gemini: bool = False, **kwargs: Any
+    ) -> Any:
+        if is_gemini:
+            return llm.client.generate_content(
+                prompt, stream=stream, generation_config=kwargs
+            )
+        else:
+            if stream:
+                return llm.client.predict_streaming(prompt[0], **kwargs)
+            return llm.client.predict(prompt[0], **kwargs)
+
+    return _completion_with_retry_inner(prompt, is_gemini, **kwargs)
+
+
+async def _acompletion_with_retry(
+    llm: VertexAI,
+    prompt: str,
+    is_gemini: bool = False,
+    run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+    **kwargs: Any,
+) -> Any:
+    """Use tenacity to retry the completion call."""
+    retry_decorator = create_retry_decorator(
+        max_retries=llm.max_retries, run_manager=run_manager
+    )
+
+    @retry_decorator
+    async def _acompletion_with_retry_inner(
+        prompt: str, is_gemini: bool = False, **kwargs: Any
+    ) -> Any:
+        if is_gemini:
+            return await llm.client.generate_content_async(
+                prompt, generation_config=kwargs
+            )
+        return await llm.client.predict_async(prompt, **kwargs)
+
+    return await _acompletion_with_retry_inner(prompt, is_gemini, **kwargs)
+
+
+class _VertexAIBase(BaseModel):
+    project: Optional[str] = None
+    "The default GCP project to use when making Vertex API calls."
+    location: str = "us-central1"
+    "The default location to use when making API calls."
+    request_parallelism: int = 5
+    "The amount of parallelism allowed for requests issued to VertexAI models. "
+    "Default is 5."
+    max_retries: int = 6
+    """The maximum number of retries to make when generating."""
+    task_executor: ClassVar[Optional[Executor]] = Field(default=None, exclude=True)
+    stop: Optional[List[str]] = None
+    "Optional list of stop words to use when generating."
+    model_name: Optional[str] = None
+    "Underlying model name."
+
+
+class _VertexAICommon(_VertexAIBase):
+    client: Any = None  #: :meta private:
+    client_preview: Any = None  #: :meta private:
+    model_name: str
+    "Underlying model name."
+    temperature: float = 0.0
+    "Sampling temperature, it controls the degree of randomness in token selection."
+    max_output_tokens: int = 128
+    "Token limit determines the maximum amount of text output from one prompt."
+    top_p: float = 0.95
+    "Tokens are selected from most probable to least until the sum of their "
+    "probabilities equals the top-p value. Top-p is ignored for Codey models."
+    top_k: int = 40
+    "How the model selects tokens for output, the next token is selected from "
+    "among the top-k most probable tokens. Top-k is ignored for Codey models."
+    credentials: Any = Field(default=None, exclude=True)
+    "The default custom credentials (google.auth.credentials.Credentials) to use "
+    "when making API calls. If not provided, credentials will be ascertained from "
+    "the environment."
+    n: int = 1
+    """How many completions to generate for each prompt."""
+    streaming: bool = False
+    """Whether to stream the results or not."""
+
+    @property
+    def _llm_type(self) -> str:
+        return "vertexai"
+
+    @property
+    def is_codey_model(self) -> bool:
+        return is_codey_model(self.model_name)
+
+    @property
+    def _is_gemini_model(self) -> bool:
+        return is_gemini_model(self.model_name)
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Gets the identifying parameters."""
+        return {**{"model_name": self.model_name}, **self._default_params}
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        params = {
+            "temperature": self.temperature,
+            "max_output_tokens": self.max_output_tokens,
+            "candidate_count": self.n,
+        }
+        if not self.is_codey_model:
+            params.update(
+                {
+                    "top_k": self.top_k,
+                    "top_p": self.top_p,
+                }
+            )
+        return params
+
+    @classmethod
+    def _init_vertexai(cls, values: Dict) -> None:
+        vertexai.init(
+            project=values.get("project"),
+            location=values.get("location"),
+            credentials=values.get("credentials"),
+        )
+        return None
+
+    def _prepare_params(
+        self,
+        stop: Optional[List[str]] = None,
+        stream: bool = False,
+        **kwargs: Any,
+    ) -> dict:
+        stop_sequences = stop or self.stop
+        params_mapping = {"n": "candidate_count"}
+        params = {params_mapping.get(k, k): v for k, v in kwargs.items()}
+        params = {**self._default_params, "stop_sequences": stop_sequences, **params}
+        if stream or self.streaming:
+            params.pop("candidate_count")
+        return params
+
+
+class VertexAI(_VertexAICommon, BaseLLM):
+    """Google Vertex AI large language models."""
+
+    model_name: str = "text-bison"
+    "The name of the Vertex AI large language model."
+    tuned_model_name: Optional[str] = None
+    "The name of a tuned model. If provided, model_name is ignored."
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that the python package exists in environment."""
+        tuned_model_name = values.get("tuned_model_name")
+        model_name = values["model_name"]
+        is_gemini = is_gemini_model(values["model_name"])
+        cls._init_vertexai(values)
+
+        if is_codey_model(model_name):
+            model_cls = CodeGenerationModel
+            preview_model_cls = PreviewCodeGenerationModel
+        elif is_gemini:
+            model_cls = GenerativeModel
+            preview_model_cls = GenerativeModel
+        else:
+            model_cls = TextGenerationModel
+            preview_model_cls = PreviewTextGenerationModel
+
+        if tuned_model_name:
+            values["client"] = model_cls.get_tuned_model(tuned_model_name)
+            values["client_preview"] = preview_model_cls.get_tuned_model(
+                tuned_model_name
+            )
+        else:
+            if is_gemini:
+                values["client"] = model_cls(model_name=model_name)
+                values["client_preview"] = preview_model_cls(model_name=model_name)
+            else:
+                values["client"] = model_cls.from_pretrained(model_name)
+                values["client_preview"] = preview_model_cls.from_pretrained(model_name)
+
+        if values["streaming"] and values["n"] > 1:
+            raise ValueError("Only one candidate can be generated with streaming!")
+        return values
+
+    def get_num_tokens(self, text: str) -> int:
+        """Get the number of tokens present in the text.
+
+        Useful for checking if an input will fit in a model's context window.
+
+        Args:
+            text: The string input to tokenize.
+
+        Returns:
+            The integer number of tokens in the text.
+        """
+        result = self.client_preview.count_tokens([text])
+        return result.total_tokens
+
+    def _response_to_generation(
+        self, response: TextGenerationResponse
+    ) -> GenerationChunk:
+        """Converts a stream response to a generation chunk."""
+        try:
+            generation_info = {
+                "is_blocked": response.is_blocked,
+                "safety_attributes": response.safety_attributes,
+            }
+        except Exception:
+            generation_info = None
+        return GenerationChunk(text=response.text, generation_info=generation_info)
+
+    def _generate(
+        self,
+        prompts: List[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        stream: Optional[bool] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        should_stream = stream if stream is not None else self.streaming
+        params = self._prepare_params(stop=stop, stream=should_stream, **kwargs)
+        generations: List[List[Generation]] = []
+        for prompt in prompts:
+            if should_stream:
+                generation = GenerationChunk(text="")
+                for chunk in self._stream(
+                    prompt, stop=stop, run_manager=run_manager, **kwargs
+                ):
+                    generation += chunk
+                generations.append([generation])
+            else:
+                res = _completion_with_retry(
+                    self,
+                    [prompt],
+                    stream=should_stream,
+                    is_gemini=self._is_gemini_model,
+                    run_manager=run_manager,
+                    **params,
+                )
+                generations.append(
+                    [self._response_to_generation(r) for r in res.candidates]
+                )
+        return LLMResult(generations=generations)
+
+    async def _agenerate(
+        self,
+        prompts: List[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        params = self._prepare_params(stop=stop, **kwargs)
+        generations = []
+        for prompt in prompts:
+            res = await _acompletion_with_retry(
+                self,
+                prompt,
+                is_gemini=self._is_gemini_model,
+                run_manager=run_manager,
+                **params,
+            )
+            generations.append(
+                [self._response_to_generation(r) for r in res.candidates]
+            )
+        return LLMResult(generations=generations)
+
+    def _stream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[GenerationChunk]:
+        params = self._prepare_params(stop=stop, stream=True, **kwargs)
+        for stream_resp in _completion_with_retry(
+            self,
+            [prompt],
+            stream=True,
+            is_gemini=self._is_gemini_model,
+            run_manager=run_manager,
+            **params,
+        ):
+            chunk = self._response_to_generation(stream_resp)
+            yield chunk
+            if run_manager:
+                run_manager.on_llm_new_token(
+                    chunk.text,
+                    chunk=chunk,
+                    verbose=self.verbose,
+                )
+
+
+class VertexAIModelGarden(_VertexAIBase, BaseLLM):
+    """Large language models served from Vertex AI Model Garden."""
+
+    client: Any = None  #: :meta private:
+    async_client: Any = None  #: :meta private:
+    endpoint_id: str
+    "A name of an endpoint where the model has been deployed."
+    allowed_model_args: Optional[List[str]] = None
+    "Allowed optional args to be passed to the model."
+    prompt_arg: str = "prompt"
+    result_arg: Optional[str] = "generated_text"
+    "Set result_arg to None if output of the model is expected to be a string."
+    "Otherwise, if it's a dict, provided an argument that contains the result."
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that the python package exists in environment."""
+
+        if not values["project"]:
+            raise ValueError(
+                "A GCP project should be provided to run inference on Model Garden!"
+            )
+
+        client_options = ClientOptions(
+            api_endpoint=f"{values['location']}-aiplatform.googleapis.com"
+        )
+        client_info = get_client_info(module="vertex-ai-model-garden")
+        values["client"] = PredictionServiceClient(
+            client_options=client_options, client_info=client_info
+        )
+        values["async_client"] = PredictionServiceAsyncClient(
+            client_options=client_options, client_info=client_info
+        )
+        return values
+
+    @property
+    def endpoint_path(self) -> str:
+        return self.client.endpoint_path(
+            project=self.project,  # type: ignore
+            location=self.location,
+            endpoint=self.endpoint_id,
+        )
+
+    @property
+    def _llm_type(self) -> str:
+        return "vertexai_model_garden"
+
+    def _prepare_request(self, prompts: List[str], **kwargs: Any) -> List["Value"]:
+        instances = []
+        for prompt in prompts:
+            if self.allowed_model_args:
+                instance = {
+                    k: v for k, v in kwargs.items() if k in self.allowed_model_args
+                }
+            else:
+                instance = {}
+            instance[self.prompt_arg] = prompt
+            instances.append(instance)
+
+        predict_instances = [
+            json_format.ParseDict(instance_dict, Value()) for instance_dict in instances
+        ]
+        return predict_instances
+
+    def _generate(
+        self,
+        prompts: List[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        """Run the LLM on the given prompt and input."""
+        instances = self._prepare_request(prompts, **kwargs)
+        response = self.client.predict(endpoint=self.endpoint_path, instances=instances)
+        return self._parse_response(response)
+
+    def _parse_response(self, predictions: "Prediction") -> LLMResult:
+        generations: List[List[Generation]] = []
+        for result in predictions.predictions:
+            generations.append(
+                [
+                    Generation(text=self._parse_prediction(prediction))
+                    for prediction in result
+                ]
+            )
+        return LLMResult(generations=generations)
+
+    def _parse_prediction(self, prediction: Any) -> str:
+        if isinstance(prediction, str):
+            return prediction
+
+        if self.result_arg:
+            try:
+                return prediction[self.result_arg]
+            except KeyError:
+                if isinstance(prediction, str):
+                    error_desc = (
+                        "Provided non-None `result_arg` (result_arg="
+                        f"{self.result_arg}). But got prediction of type "
+                        f"{type(prediction)} instead of dict. Most probably, you"
+                        "need to set `result_arg=None` during VertexAIModelGarden "
+                        "initialization."
+                    )
+                    raise ValueError(error_desc)
+                else:
+                    raise ValueError(f"{self.result_arg} key not found in prediction!")
+
+        return prediction
+
+    async def _agenerate(
+        self,
+        prompts: List[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        """Run the LLM on the given prompt and input."""
+        instances = self._prepare_request(prompts, **kwargs)
+        response = await self.async_client.predict(
+            endpoint=self.endpoint_path, instances=instances
+        )
+        return self._parse_response(response)
diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/py.typed b/libs/partners/google-vertexai/langchain_google_vertexai/py.typed
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/partners/google-vertexai/poetry.lock b/libs/partners/google-vertexai/poetry.lock
new file mode 100644
index 00000000000..3fd6f04c249
--- /dev/null
+++ b/libs/partners/google-vertexai/poetry.lock
@@ -0,0 +1,1444 @@
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
+[[package]]
+name = "annotated-types"
+version = "0.6.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
+    {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
+
+[[package]]
+name = "anyio"
+version = "4.2.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
+
+[[package]]
+name = "cachetools"
+version = "5.3.2"
+description = "Extensible memoizing collections and decorators"
+optional = false
+python-versions = ">=3.7"
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+]
+
+[[package]]
+name = "certifi"
+version = "2023.11.17"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
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+    {file = "watchdog-3.0.0-py3-none-win_amd64.whl", hash = "sha256:4c9956d27be0bb08fc5f30d9d0179a855436e655f046d288e2bcc11adfae893c"},
+    {file = "watchdog-3.0.0-py3-none-win_ia64.whl", hash = "sha256:5d9f3a10e02d7371cd929b5d8f11e87d4bad890212ed3901f9b4d68767bee759"},
+    {file = "watchdog-3.0.0.tar.gz", hash = "sha256:4d98a320595da7a7c5a18fc48cb633c2e73cda78f93cac2ef42d42bf609a33f9"},
+]
+
+[package.extras]
+watchmedo = ["PyYAML (>=3.10)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.8.1,<4.0"
+content-hash = "7fcbc6833c982cb513d5655481487edf16d011a4366b7612bd2f0da98ade21b0"
diff --git a/libs/partners/google-vertexai/pyproject.toml b/libs/partners/google-vertexai/pyproject.toml
new file mode 100644
index 00000000000..4553ea5e2aa
--- /dev/null
+++ b/libs/partners/google-vertexai/pyproject.toml
@@ -0,0 +1,94 @@
+[tool.poetry]
+name = "langchain-google-vertexai"
+version = "0.0.1"
+description = "An integration package connecting GoogleVertexAI and LangChain"
+authors = []
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain-core = ">=0.1,<0.2"
+google-cloud-aiplatform = "1.38.1"
+google-cloud-storage = "^2.14.0"
+types-requests = "^2.31.0.20231231"
+types-protobuf = "^4.24.0.4"
+
+[tool.poetry.group.test]
+optional = true
+
+[tool.poetry.group.test.dependencies]
+pytest = "^7.3.0"
+freezegun = "^1.2.2"
+pytest-mock  = "^3.10.0"
+syrupy = "^4.0.2"
+pytest-watcher = "^0.3.4"
+pytest-asyncio = "^0.21.1"
+langchain-core = {path = "../../core", develop = true}
+types-requests = "^2.31.0.20231231"
+types-protobuf = "^4.24.0.4"
+
+[tool.poetry.group.codespell]
+optional = true
+
+[tool.poetry.group.codespell.dependencies]
+codespell = "^2.2.0"
+
+[tool.poetry.group.test_integration]
+optional = true
+
+[tool.poetry.group.test_integration.dependencies]
+
+[tool.poetry.group.lint]
+optional = true
+
+[tool.poetry.group.lint.dependencies]
+ruff = "^0.1.5"
+
+[tool.poetry.group.typing.dependencies]
+mypy = "^0.991"
+langchain-core = {path = "../../core", develop = true}
+
+[tool.poetry.group.dev]
+optional = true
+
+[tool.poetry.group.dev.dependencies]
+langchain-core = {path = "../../core", develop = true}
+
+[tool.ruff]
+select = [
+  "E",  # pycodestyle
+  "F",  # pyflakes
+  "I",  # isort
+]
+
+[tool.mypy]
+disallow_untyped_defs = "True"
+
+[tool.coverage.run]
+omit = [
+    "tests/*",
+]
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.pytest.ini_options]
+# --strict-markers will raise errors on unknown marks.
+# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
+#
+# https://docs.pytest.org/en/7.1.x/reference/reference.html
+# --strict-config       any warnings encountered while parsing the `pytest`
+#                       section of the configuration file raise errors.
+#
+# https://github.com/tophat/syrupy
+# --snapshot-warn-unused    Prints a warning on unused snapshots rather than fail the test suite.
+addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
+# Registering custom markers.
+# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers
+markers = [
+  "requires: mark tests as requiring a specific library",
+  "asyncio: mark tests as requiring asyncio",
+  "compile: mark placeholder test used to compile integration tests without running them",
+]
+asyncio_mode = "auto"
diff --git a/libs/partners/google-vertexai/scripts/check_imports.py b/libs/partners/google-vertexai/scripts/check_imports.py
new file mode 100644
index 00000000000..fd21a4975b7
--- /dev/null
+++ b/libs/partners/google-vertexai/scripts/check_imports.py
@@ -0,0 +1,17 @@
+import sys
+import traceback
+from importlib.machinery import SourceFileLoader
+
+if __name__ == "__main__":
+    files = sys.argv[1:]
+    has_failure = False
+    for file in files:
+        try:
+            SourceFileLoader("x", file).load_module()
+        except Exception:
+            has_faillure = True
+            print(file)
+            traceback.print_exc()
+            print()
+
+    sys.exit(1 if has_failure else 0)
diff --git a/libs/partners/google-vertexai/scripts/check_pydantic.sh b/libs/partners/google-vertexai/scripts/check_pydantic.sh
new file mode 100755
index 00000000000..06b5bb81ae2
--- /dev/null
+++ b/libs/partners/google-vertexai/scripts/check_pydantic.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+#
+# This script searches for lines starting with "import pydantic" or "from pydantic"
+# in tracked files within a Git repository.
+#
+# Usage: ./scripts/check_pydantic.sh /path/to/repository
+
+# Check if a path argument is provided
+if [ $# -ne 1 ]; then
+  echo "Usage: $0 /path/to/repository"
+  exit 1
+fi
+
+repository_path="$1"
+
+# Search for lines matching the pattern within the specified repository
+result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
+
+# Check if any matching lines were found
+if [ -n "$result" ]; then
+  echo "ERROR: The following lines need to be updated:"
+  echo "$result"
+  echo "Please replace the code with an import from langchain_core.pydantic_v1."
+  echo "For example, replace 'from pydantic import BaseModel'"
+  echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
+  exit 1
+fi
diff --git a/libs/partners/google-vertexai/scripts/lint_imports.sh b/libs/partners/google-vertexai/scripts/lint_imports.sh
new file mode 100755
index 00000000000..695613c7ba8
--- /dev/null
+++ b/libs/partners/google-vertexai/scripts/lint_imports.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+set -eu
+
+# Initialize a variable to keep track of errors
+errors=0
+
+# make sure not importing from langchain or langchain_experimental
+git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
+git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
+
+# Decide on an exit status based on the errors
+if [ "$errors" -gt 0 ]; then
+    exit 1
+else
+    exit 0
+fi
diff --git a/libs/partners/google-vertexai/tests/__init__.py b/libs/partners/google-vertexai/tests/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/partners/google-vertexai/tests/integration_tests/__init__.py b/libs/partners/google-vertexai/tests/integration_tests/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py b/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py
new file mode 100644
index 00000000000..5bf65d99b7d
--- /dev/null
+++ b/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py
@@ -0,0 +1,176 @@
+"""Test ChatGoogleVertexAI chat model."""
+import pytest
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    HumanMessage,
+    SystemMessage,
+)
+from langchain_core.outputs import LLMResult
+
+from langchain_google_vertexai.chat_models import ChatVertexAI
+
+model_names_to_test = [None, "codechat-bison", "chat-bison", "gemini-pro"]
+
+
+@pytest.mark.parametrize("model_name", model_names_to_test)
+def test_initialization(model_name: str) -> None:
+    """Test chat model initialization."""
+    if model_name:
+        model = ChatVertexAI(model_name=model_name)
+    else:
+        model = ChatVertexAI()
+    assert model._llm_type == "vertexai"
+    try:
+        assert model.model_name == model.client._model_id
+    except AttributeError:
+        assert model.model_name == model.client._model_name.split("/")[-1]
+
+
+@pytest.mark.parametrize("model_name", model_names_to_test)
+def test_vertexai_single_call(model_name: str) -> None:
+    if model_name:
+        model = ChatVertexAI(model_name=model_name)
+    else:
+        model = ChatVertexAI()
+    message = HumanMessage(content="Hello")
+    response = model([message])
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+
+# mark xfail because Vertex API randomly doesn't respect
+# the n/candidate_count parameter
+@pytest.mark.xfail
+def test_candidates() -> None:
+    model = ChatVertexAI(model_name="chat-bison@001", temperature=0.3, n=2)
+    message = HumanMessage(content="Hello")
+    response = model.generate(messages=[[message]])
+    assert isinstance(response, LLMResult)
+    assert len(response.generations) == 1
+    assert len(response.generations[0]) == 2
+
+
+@pytest.mark.parametrize("model_name", ["chat-bison@001", "gemini-pro"])
+async def test_vertexai_agenerate(model_name: str) -> None:
+    model = ChatVertexAI(temperature=0, model_name=model_name)
+    message = HumanMessage(content="Hello")
+    response = await model.agenerate([[message]])
+    assert isinstance(response, LLMResult)
+    assert isinstance(response.generations[0][0].message, AIMessage)  # type: ignore
+
+    sync_response = model.generate([[message]])
+    assert response.generations[0][0] == sync_response.generations[0][0]
+
+
+@pytest.mark.parametrize("model_name", ["chat-bison@001", "gemini-pro"])
+def test_vertexai_stream(model_name: str) -> None:
+    model = ChatVertexAI(temperature=0, model_name=model_name)
+    message = HumanMessage(content="Hello")
+
+    sync_response = model.stream([message])
+    for chunk in sync_response:
+        assert isinstance(chunk, AIMessageChunk)
+
+
+def test_vertexai_single_call_with_context() -> None:
+    model = ChatVertexAI()
+    raw_context = (
+        "My name is Ned. You are my personal assistant. My favorite movies "
+        "are Lord of the Rings and Hobbit."
+    )
+    question = (
+        "Hello, could you recommend a good movie for me to watch this evening, please?"
+    )
+    context = SystemMessage(content=raw_context)
+    message = HumanMessage(content=question)
+    response = model([context, message])
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+
+def test_multimodal() -> None:
+    llm = ChatVertexAI(model_name="gemini-pro-vision")
+    gcs_url = (
+        "gs://cloud-samples-data/generative-ai/image/"
+        "320px-Felis_catus-cat_on_snow.jpg"
+    )
+    image_message = {
+        "type": "image_url",
+        "image_url": {"url": gcs_url},
+    }
+    text_message = {
+        "type": "text",
+        "text": "What is shown in this image?",
+    }
+    message = HumanMessage(content=[text_message, image_message])
+    output = llm([message])
+    assert isinstance(output.content, str)
+
+
+def test_multimodal_history() -> None:
+    llm = ChatVertexAI(model_name="gemini-pro-vision")
+    gcs_url = (
+        "gs://cloud-samples-data/generative-ai/image/"
+        "320px-Felis_catus-cat_on_snow.jpg"
+    )
+    image_message = {
+        "type": "image_url",
+        "image_url": {"url": gcs_url},
+    }
+    text_message = {
+        "type": "text",
+        "text": "What is shown in this image?",
+    }
+    message1 = HumanMessage(content=[text_message, image_message])
+    message2 = AIMessage(
+        content=(
+            "This is a picture of a cat in the snow. The cat is a tabby cat, which is "
+            "a type of cat with a striped coat. The cat is standing in the snow, and "
+            "its fur is covered in snow."
+        )
+    )
+    message3 = HumanMessage(content="What time of day is it?")
+    response = llm([message1, message2, message3])
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+
+def test_vertexai_single_call_with_examples() -> None:
+    model = ChatVertexAI()
+    raw_context = "My name is Ned. You are my personal assistant."
+    question = "2+2"
+    text_question, text_answer = "4+4", "8"
+    inp = HumanMessage(content=text_question)
+    output = AIMessage(content=text_answer)
+    context = SystemMessage(content=raw_context)
+    message = HumanMessage(content=question)
+    response = model([context, message], examples=[inp, output])
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+
+@pytest.mark.parametrize("model_name", model_names_to_test)
+def test_vertexai_single_call_with_history(model_name: str) -> None:
+    if model_name:
+        model = ChatVertexAI(model_name=model_name)
+    else:
+        model = ChatVertexAI()
+    text_question1, text_answer1 = "How much is 2+2?", "4"
+    text_question2 = "How much is 3+3?"
+    message1 = HumanMessage(content=text_question1)
+    message2 = AIMessage(content=text_answer1)
+    message3 = HumanMessage(content=text_question2)
+    response = model([message1, message2, message3])
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+
+def test_vertexai_single_call_fails_no_message() -> None:
+    chat = ChatVertexAI()
+    with pytest.raises(ValueError) as exc_info:
+        _ = chat([])
+    assert (
+        str(exc_info.value)
+        == "You should provide at least one message to start the chat!"
+    )
diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_compile.py b/libs/partners/google-vertexai/tests/integration_tests/test_compile.py
new file mode 100644
index 00000000000..33ecccdfa0f
--- /dev/null
+++ b/libs/partners/google-vertexai/tests/integration_tests/test_compile.py
@@ -0,0 +1,7 @@
+import pytest
+
+
+@pytest.mark.compile
+def test_placeholder() -> None:
+    """Used for compiling integration tests without running any real tests."""
+    pass
diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_embeddings.py b/libs/partners/google-vertexai/tests/integration_tests/test_embeddings.py
new file mode 100644
index 00000000000..42495629b20
--- /dev/null
+++ b/libs/partners/google-vertexai/tests/integration_tests/test_embeddings.py
@@ -0,0 +1,70 @@
+"""Test Vertex AI API wrapper.
+
+Your end-user credentials would be used to make the calls (make sure you've run
+`gcloud auth login` first).
+"""
+import pytest
+
+from langchain_google_vertexai.embeddings import VertexAIEmbeddings
+
+
+def test_initialization() -> None:
+    """Test embedding model initialization."""
+    VertexAIEmbeddings()
+
+
+def test_langchain_google_vertexai_embedding_documents() -> None:
+    documents = ["foo bar"]
+    model = VertexAIEmbeddings()
+    output = model.embed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) == 768
+    assert model.model_name == model.client._model_id
+    assert model.model_name == "textembedding-gecko@001"
+
+
+def test_langchain_google_vertexai_embedding_query() -> None:
+    document = "foo bar"
+    model = VertexAIEmbeddings()
+    output = model.embed_query(document)
+    assert len(output) == 768
+
+
+def test_langchain_google_vertexai_large_batches() -> None:
+    documents = ["foo bar" for _ in range(0, 251)]
+    model_uscentral1 = VertexAIEmbeddings(location="us-central1")
+    model_asianortheast1 = VertexAIEmbeddings(location="asia-northeast1")
+    model_uscentral1.embed_documents(documents)
+    model_asianortheast1.embed_documents(documents)
+    assert model_uscentral1.instance["batch_size"] >= 250
+    assert model_asianortheast1.instance["batch_size"] < 50
+
+
+def test_langchain_google_vertexai_paginated_texts() -> None:
+    documents = [
+        "foo bar",
+        "foo baz",
+        "bar foo",
+        "baz foo",
+        "bar bar",
+        "foo foo",
+        "baz baz",
+        "baz bar",
+    ]
+    model = VertexAIEmbeddings()
+    output = model.embed_documents(documents)
+    assert len(output) == 8
+    assert len(output[0]) == 768
+    assert model.model_name == model.client._model_id
+
+
+def test_warning(caplog: pytest.LogCaptureFixture) -> None:
+    _ = VertexAIEmbeddings()
+    assert len(caplog.records) == 1
+    record = caplog.records[0]
+    assert record.levelname == "WARNING"
+    expected_message = (
+        "Model_name will become a required arg for VertexAIEmbeddings starting from "
+        "Feb-01-2024. Currently the default is set to textembedding-gecko@001"
+    )
+    assert record.message == expected_message
diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_llms.py b/libs/partners/google-vertexai/tests/integration_tests/test_llms.py
new file mode 100644
index 00000000000..14f84c06162
--- /dev/null
+++ b/libs/partners/google-vertexai/tests/integration_tests/test_llms.py
@@ -0,0 +1,175 @@
+"""Test Vertex AI API wrapper.
+
+Your end-user credentials would be used to make the calls (make sure you've run 
+`gcloud auth login` first).
+"""
+import os
+from typing import Optional
+
+import pytest
+from langchain_core.outputs import LLMResult
+
+from langchain_google_vertexai.llms import VertexAI, VertexAIModelGarden
+
+model_names_to_test = ["text-bison@001", "gemini-pro"]
+model_names_to_test_with_default = [None] + model_names_to_test
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    model_names_to_test_with_default,
+)
+def test_vertex_initialization(model_name: str) -> None:
+    llm = VertexAI(model_name=model_name) if model_name else VertexAI()
+    assert llm._llm_type == "vertexai"
+    try:
+        assert llm.model_name == llm.client._model_id
+    except AttributeError:
+        assert llm.model_name == llm.client._model_name.split("/")[-1]
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    model_names_to_test_with_default,
+)
+def test_vertex_call(model_name: str) -> None:
+    llm = (
+        VertexAI(model_name=model_name, temperature=0)
+        if model_name
+        else VertexAI(temperature=0.0)
+    )
+    output = llm("Say foo:")
+    assert isinstance(output, str)
+
+
+def test_vertex_generate() -> None:
+    llm = VertexAI(temperature=0.3, n=2, model_name="text-bison@001")
+    output = llm.generate(["Say foo:"])
+    assert isinstance(output, LLMResult)
+    assert len(output.generations) == 1
+    assert len(output.generations[0]) == 2
+
+
+def test_vertex_generate_code() -> None:
+    llm = VertexAI(temperature=0.3, n=2, model_name="code-bison@001")
+    output = llm.generate(["generate a python method that says foo:"])
+    assert isinstance(output, LLMResult)
+    assert len(output.generations) == 1
+    assert len(output.generations[0]) == 2
+
+
+async def test_vertex_agenerate() -> None:
+    llm = VertexAI(temperature=0)
+    output = await llm.agenerate(["Please say foo:"])
+    assert isinstance(output, LLMResult)
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    model_names_to_test_with_default,
+)
+def test_stream(model_name: str) -> None:
+    llm = (
+        VertexAI(temperature=0, model_name=model_name)
+        if model_name
+        else VertexAI(temperature=0)
+    )
+    for token in llm.stream("I'm Pickle Rick"):
+        assert isinstance(token, str)
+
+
+async def test_vertex_consistency() -> None:
+    llm = VertexAI(temperature=0)
+    output = llm.generate(["Please say foo:"])
+    streaming_output = llm.generate(["Please say foo:"], stream=True)
+    async_output = await llm.agenerate(["Please say foo:"])
+    assert output.generations[0][0].text == streaming_output.generations[0][0].text
+    assert output.generations[0][0].text == async_output.generations[0][0].text
+
+
+@pytest.mark.parametrize(
+    "endpoint_os_variable_name,result_arg",
+    [("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
+)
+def test_model_garden(
+    endpoint_os_variable_name: str, result_arg: Optional[str]
+) -> None:
+    """In order to run this test, you should provide endpoint names.
+
+    Example:
+    export FALCON_ENDPOINT_ID=...
+    export LLAMA_ENDPOINT_ID=...
+    export PROJECT=...
+    """
+    endpoint_id = os.environ[endpoint_os_variable_name]
+    project = os.environ["PROJECT"]
+    location = "europe-west4"
+    llm = VertexAIModelGarden(
+        endpoint_id=endpoint_id,
+        project=project,
+        result_arg=result_arg,
+        location=location,
+    )
+    output = llm("What is the meaning of life?")
+    assert isinstance(output, str)
+    assert llm._llm_type == "vertexai_model_garden"
+
+
+@pytest.mark.parametrize(
+    "endpoint_os_variable_name,result_arg",
+    [("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
+)
+def test_model_garden_generate(
+    endpoint_os_variable_name: str, result_arg: Optional[str]
+) -> None:
+    """In order to run this test, you should provide endpoint names.
+
+    Example:
+    export FALCON_ENDPOINT_ID=...
+    export LLAMA_ENDPOINT_ID=...
+    export PROJECT=...
+    """
+    endpoint_id = os.environ[endpoint_os_variable_name]
+    project = os.environ["PROJECT"]
+    location = "europe-west4"
+    llm = VertexAIModelGarden(
+        endpoint_id=endpoint_id,
+        project=project,
+        result_arg=result_arg,
+        location=location,
+    )
+    output = llm.generate(["What is the meaning of life?", "How much is 2+2"])
+    assert isinstance(output, LLMResult)
+    assert len(output.generations) == 2
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "endpoint_os_variable_name,result_arg",
+    [("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
+)
+async def test_model_garden_agenerate(
+    endpoint_os_variable_name: str, result_arg: Optional[str]
+) -> None:
+    endpoint_id = os.environ[endpoint_os_variable_name]
+    project = os.environ["PROJECT"]
+    location = "europe-west4"
+    llm = VertexAIModelGarden(
+        endpoint_id=endpoint_id,
+        project=project,
+        result_arg=result_arg,
+        location=location,
+    )
+    output = await llm.agenerate(["What is the meaning of life?", "How much is 2+2"])
+    assert isinstance(output, LLMResult)
+    assert len(output.generations) == 2
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    model_names_to_test,
+)
+def test_vertex_call_count_tokens(model_name: str) -> None:
+    llm = VertexAI(model_name=model_name)
+    output = llm.get_num_tokens("How are you?")
+    assert output == 4
diff --git a/libs/partners/google-vertexai/tests/unit_tests/__init__.py b/libs/partners/google-vertexai/tests/unit_tests/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py b/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py
new file mode 100644
index 00000000000..bff39ee4318
--- /dev/null
+++ b/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py
@@ -0,0 +1,112 @@
+"""Test chat model integration."""
+from typing import Optional
+from unittest.mock import MagicMock, Mock, patch
+
+import pytest
+from langchain_core.messages import (
+    AIMessage,
+    HumanMessage,
+    SystemMessage,
+)
+from vertexai.language_models import ChatMessage, InputOutputTextPair  # type: ignore
+
+from langchain_google_vertexai.chat_models import (
+    ChatVertexAI,
+    _parse_chat_history,
+    _parse_examples,
+)
+
+
+def test_parse_examples_correct() -> None:
+    text_question = (
+        "Hello, could you recommend a good movie for me to watch this evening, please?"
+    )
+    question = HumanMessage(content=text_question)
+    text_answer = (
+        "Sure, You might enjoy The Lord of the Rings: The Fellowship of the Ring "
+        "(2001): This is the first movie in the Lord of the Rings trilogy."
+    )
+    answer = AIMessage(content=text_answer)
+    examples = _parse_examples([question, answer, question, answer])
+    assert len(examples) == 2
+    assert examples == [
+        InputOutputTextPair(input_text=text_question, output_text=text_answer),
+        InputOutputTextPair(input_text=text_question, output_text=text_answer),
+    ]
+
+
+def test_parse_examples_failes_wrong_sequence() -> None:
+    with pytest.raises(ValueError) as exc_info:
+        _ = _parse_examples([AIMessage(content="a")])
+    assert (
+        str(exc_info.value)
+        == "Expect examples to have an even amount of messages, got 1."
+    )
+
+
+@pytest.mark.parametrize("stop", [None, "stop1"])
+def test_vertexai_args_passed(stop: Optional[str]) -> None:
+    response_text = "Goodbye"
+    user_prompt = "Hello"
+    prompt_params = {
+        "max_output_tokens": 1,
+        "temperature": 10000.0,
+        "top_k": 10,
+        "top_p": 0.5,
+    }
+
+    # Mock the library to ensure the args are passed correctly
+    with patch("vertexai._model_garden._model_garden_models._from_pretrained") as mg:
+        mock_response = MagicMock()
+        mock_response.candidates = [Mock(text=response_text)]
+        mock_chat = MagicMock()
+        mock_send_message = MagicMock(return_value=mock_response)
+        mock_chat.send_message = mock_send_message
+
+        mock_model = MagicMock()
+        mock_start_chat = MagicMock(return_value=mock_chat)
+        mock_model.start_chat = mock_start_chat
+        mg.return_value = mock_model
+
+        model = ChatVertexAI(**prompt_params)
+        message = HumanMessage(content=user_prompt)
+        if stop:
+            response = model([message], stop=[stop])
+        else:
+            response = model([message])
+
+        assert response.content == response_text
+        mock_send_message.assert_called_once_with(user_prompt, candidate_count=1)
+        expected_stop_sequence = [stop] if stop else None
+        mock_start_chat.assert_called_once_with(
+            context=None,
+            message_history=[],
+            **prompt_params,
+            stop_sequences=expected_stop_sequence,
+        )
+
+
+def test_parse_chat_history_correct() -> None:
+    text_context = (
+        "My name is Ned. You are my personal assistant. My "
+        "favorite movies are Lord of the Rings and Hobbit."
+    )
+    context = SystemMessage(content=text_context)
+    text_question = (
+        "Hello, could you recommend a good movie for me to watch this evening, please?"
+    )
+    question = HumanMessage(content=text_question)
+    text_answer = (
+        "Sure, You might enjoy The Lord of the Rings: The Fellowship of the Ring "
+        "(2001): This is the first movie in the Lord of the Rings trilogy."
+    )
+    answer = AIMessage(content=text_answer)
+    history = _parse_chat_history([context, question, answer, question, answer])
+    assert history.context == context.content
+    assert len(history.history) == 4
+    assert history.history == [
+        ChatMessage(content=text_question, author="user"),
+        ChatMessage(content=text_answer, author="bot"),
+        ChatMessage(content=text_question, author="user"),
+        ChatMessage(content=text_answer, author="bot"),
+    ]
diff --git a/libs/partners/google-vertexai/tests/unit_tests/test_imports.py b/libs/partners/google-vertexai/tests/unit_tests/test_imports.py
new file mode 100644
index 00000000000..016d6e21c73
--- /dev/null
+++ b/libs/partners/google-vertexai/tests/unit_tests/test_imports.py
@@ -0,0 +1,7 @@
+from langchain_google_vertexai import __all__
+
+EXPECTED_ALL = ["ChatVertexAI", "VertexAIEmbeddings", "VertexAI", "VertexAIModelGarden"]
+
+
+def test_all_imports() -> None:
+    assert sorted(EXPECTED_ALL) == sorted(__all__)